Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "66" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 33 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 33 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460016 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.258121 | 1.006882 | 7.147199 | 2.642795 | 0.397865 | 0.391747 | 6.971274 | 4.800138 | 0.5231 | 0.5731 | 0.3767 | nan | nan |
| 2460015 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.438259 | 1.148960 | 7.127587 | 3.329596 | 0.303415 | -0.017908 | 6.346841 | 4.556831 | 0.5370 | 0.5772 | 0.3712 | nan | nan |
| 2460014 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.488469 | 0.669688 | 5.832324 | 2.712308 | 1.964993 | -0.301367 | 3.373787 | 2.157787 | 0.4955 | 0.5580 | 0.3835 | nan | nan |
| 2460013 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.693129 | 0.910561 | 7.157304 | 3.936419 | 1.811575 | 0.203114 | 8.724074 | 4.035604 | 0.5301 | 0.5770 | 0.3766 | nan | nan |
| 2460012 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 0.886430 | 0.073533 | -1.330778 | 4.367779 | 1.716211 | 2.060308 | -0.118813 | 2.411242 | 0.2083 | 0.1848 | -0.2802 | nan | nan |
| 2460011 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 0.616255 | 0.039224 | -1.938024 | 5.823270 | 2.922218 | 0.988799 | -0.265985 | 1.594514 | 0.2117 | 0.1939 | -0.2814 | nan | nan |
| 2460010 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 0.721926 | 0.033851 | -1.265837 | 5.090665 | 3.190727 | 1.214474 | -0.454407 | 1.057411 | 0.2088 | 0.1892 | -0.2804 | nan | nan |
| 2460009 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 0.786350 | 0.279319 | -1.514280 | 5.290002 | 2.020858 | 1.208512 | -0.515070 | 1.714824 | 0.2171 | 0.2004 | -0.2846 | nan | nan |
| 2460008 | digital_ok | 100.00% | 0.00% | 0.00% | 99.73% | - | - | 0.741762 | 0.147496 | -1.766950 | 5.498909 | 1.592803 | 2.406589 | 1.867143 | 4.734125 | 0.3062 | 0.2966 | -0.2598 | nan | nan |
| 2460007 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 1.014261 | 0.403471 | -1.356767 | 4.091930 | 1.731271 | 0.910923 | -0.396468 | 2.103736 | 0.2375 | 0.2233 | -0.2788 | nan | nan |
| 2459999 | digital_ok | 0.00% | 100.00% | 99.92% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0871 | 0.1701 | 0.1286 | nan | nan |
| 2459998 | digital_ok | 0.00% | 0.00% | 0.00% | 99.95% | - | - | 0.768669 | 0.661184 | -1.179232 | 3.332378 | 2.374906 | 2.216138 | -0.811748 | 1.507345 | 0.2107 | 0.1919 | -0.3028 | nan | nan |
| 2459997 | digital_ok | 0.00% | 0.00% | 0.00% | 100.00% | - | - | 0.637693 | 0.682477 | -1.182420 | 3.578618 | 2.415052 | 1.052159 | -0.301375 | 3.796874 | 0.2158 | 0.2021 | -0.3073 | nan | nan |
| 2459996 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.060278 | 1.918696 | -1.326561 | -0.366779 | 0.672145 | 0.008351 | -0.532777 | 1.307832 | 0.6156 | 0.6315 | 0.4109 | nan | nan |
| 2459995 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.912469 | 1.785144 | -1.519616 | -0.826908 | 3.049810 | -0.199829 | -0.646349 | 0.689381 | 0.6063 | 0.6220 | 0.4003 | nan | nan |
| 2459994 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.442798 | 1.848814 | -1.269593 | -0.582097 | 2.076319 | -0.153258 | -0.556665 | 0.429122 | 0.6046 | 0.6186 | 0.3953 | nan | nan |
| 2459993 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.199644 | 2.070633 | -1.330678 | -0.753458 | 2.684937 | -0.348263 | -0.470180 | 0.501636 | 0.5853 | 0.6258 | 0.4166 | nan | nan |
| 2459991 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.171127 | 2.029376 | -1.275756 | -0.753086 | 2.403250 | 0.124337 | -0.414832 | 0.364674 | 0.6183 | 0.6280 | 0.3998 | nan | nan |
| 2459990 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.136232 | 1.665470 | -1.129019 | -0.838673 | 2.740796 | 0.143892 | -0.207001 | 0.461286 | 0.6155 | 0.6281 | 0.3986 | nan | nan |
| 2459989 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.662749 | 1.673397 | -0.990700 | -0.510851 | 2.458161 | 0.370699 | -0.571593 | 0.073824 | 0.6127 | 0.6268 | 0.4003 | nan | nan |
| 2459988 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.423693 | 2.134152 | -1.265078 | -0.992529 | 2.314810 | 0.008011 | -0.673156 | 0.101080 | 0.6090 | 0.6247 | 0.3929 | nan | nan |
| 2459987 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.126094 | 1.532421 | -1.346781 | -0.875264 | 1.650263 | -0.131354 | -0.437924 | 0.745485 | 0.6194 | 0.6329 | 0.3915 | nan | nan |
| 2459986 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.049339 | 2.087282 | -1.446838 | -1.162032 | 2.464244 | -0.269408 | -0.757897 | -1.250872 | 0.6404 | 0.6582 | 0.3428 | nan | nan |
| 2459985 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.201080 | 2.121194 | -1.400123 | -0.948399 | 1.598057 | -0.054327 | -0.006813 | 0.800957 | 0.6162 | 0.6310 | 0.3984 | nan | nan |
| 2459984 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.728089 | 1.757630 | -1.322643 | -0.765321 | -0.510202 | 12.723418 | -0.605465 | 1.197284 | 0.6263 | 0.6445 | 0.3808 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.766469 | 1.619037 | -1.284360 | -1.115753 | 2.004645 | -0.313592 | -0.618665 | -0.919318 | 0.6372 | 0.6631 | 0.3429 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.316803 | 0.697573 | -0.735851 | -0.679512 | 1.843496 | 0.175713 | -0.406328 | -0.746889 | 0.6989 | 0.7053 | 0.2929 | nan | nan |
| 2459981 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.647061 | 1.314357 | -1.389505 | -1.384671 | 2.548062 | -0.642012 | -0.716697 | -0.424224 | 0.6165 | 0.6347 | 0.3962 | nan | nan |
| 2459980 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.474431 | 1.102310 | -1.434436 | -1.116515 | 1.904206 | -0.394150 | -0.985730 | -1.035818 | 0.6626 | 0.6779 | 0.3179 | nan | nan |
| 2459979 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.478320 | 1.203975 | -1.332878 | -1.159263 | 2.212114 | -0.285851 | -0.812032 | -0.530970 | 0.6088 | 0.6308 | 0.3983 | nan | nan |
| 2459978 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.637866 | 1.260284 | -1.409298 | -1.287564 | 2.330524 | -0.217539 | -0.706679 | -0.218388 | 0.6102 | 0.6304 | 0.4043 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.634514 | 1.341861 | -1.425297 | -1.128046 | 2.141497 | -0.764833 | -0.721266 | -0.271560 | 0.5725 | 0.5920 | 0.3686 | nan | nan |
| 2459976 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.508577 | 1.028170 | -1.290859 | -1.436914 | 3.208537 | -0.641899 | -0.992429 | -0.701754 | 0.6162 | 0.6362 | 0.3928 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Power | 7.147199 | 1.006882 | 0.258121 | 2.642795 | 7.147199 | 0.391747 | 0.397865 | 4.800138 | 6.971274 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Power | 7.127587 | 1.148960 | 0.438259 | 3.329596 | 7.127587 | -0.017908 | 0.303415 | 4.556831 | 6.346841 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Power | 5.832324 | 0.488469 | 0.669688 | 5.832324 | 2.712308 | 1.964993 | -0.301367 | 3.373787 | 2.157787 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Discontinuties | 8.724074 | 0.693129 | 0.910561 | 7.157304 | 3.936419 | 1.811575 | 0.203114 | 8.724074 | 4.035604 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Power | 4.367779 | 0.886430 | 0.073533 | -1.330778 | 4.367779 | 1.716211 | 2.060308 | -0.118813 | 2.411242 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Power | 5.823270 | 0.616255 | 0.039224 | -1.938024 | 5.823270 | 2.922218 | 0.988799 | -0.265985 | 1.594514 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Power | 5.090665 | 0.721926 | 0.033851 | -1.265837 | 5.090665 | 3.190727 | 1.214474 | -0.454407 | 1.057411 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Power | 5.290002 | 0.786350 | 0.279319 | -1.514280 | 5.290002 | 2.020858 | 1.208512 | -0.515070 | 1.714824 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Power | 5.498909 | 0.147496 | 0.741762 | 5.498909 | -1.766950 | 2.406589 | 1.592803 | 4.734125 | 1.867143 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Power | 4.091930 | 1.014261 | 0.403471 | -1.356767 | 4.091930 | 1.731271 | 0.910923 | -0.396468 | 2.103736 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Power | 3.332378 | 0.768669 | 0.661184 | -1.179232 | 3.332378 | 2.374906 | 2.216138 | -0.811748 | 1.507345 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Discontinuties | 3.796874 | 0.637693 | 0.682477 | -1.182420 | 3.578618 | 2.415052 | 1.052159 | -0.301375 | 3.796874 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 1.918696 | 1.060278 | 1.918696 | -1.326561 | -0.366779 | 0.672145 | 0.008351 | -0.532777 | 1.307832 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 3.049810 | 0.912469 | 1.785144 | -1.519616 | -0.826908 | 3.049810 | -0.199829 | -0.646349 | 0.689381 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.076319 | 1.442798 | 1.848814 | -1.269593 | -0.582097 | 2.076319 | -0.153258 | -0.556665 | 0.429122 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.684937 | 1.199644 | 2.070633 | -1.330678 | -0.753458 | 2.684937 | -0.348263 | -0.470180 | 0.501636 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.403250 | 1.171127 | 2.029376 | -1.275756 | -0.753086 | 2.403250 | 0.124337 | -0.414832 | 0.364674 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.740796 | 1.665470 | 1.136232 | -0.838673 | -1.129019 | 0.143892 | 2.740796 | 0.461286 | -0.207001 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.458161 | 1.673397 | 1.662749 | -0.510851 | -0.990700 | 0.370699 | 2.458161 | 0.073824 | -0.571593 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.314810 | 2.134152 | 1.423693 | -0.992529 | -1.265078 | 0.008011 | 2.314810 | 0.101080 | -0.673156 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 1.650263 | 1.126094 | 1.532421 | -1.346781 | -0.875264 | 1.650263 | -0.131354 | -0.437924 | 0.745485 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.464244 | 2.087282 | 1.049339 | -1.162032 | -1.446838 | -0.269408 | 2.464244 | -1.250872 | -0.757897 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Shape | 2.121194 | 2.121194 | 1.201080 | -0.948399 | -1.400123 | -0.054327 | 1.598057 | 0.800957 | -0.006813 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | nn Temporal Variability | 12.723418 | 0.728089 | 1.757630 | -1.322643 | -0.765321 | -0.510202 | 12.723418 | -0.605465 | 1.197284 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.004645 | 0.766469 | 1.619037 | -1.284360 | -1.115753 | 2.004645 | -0.313592 | -0.618665 | -0.919318 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 1.843496 | 0.316803 | 0.697573 | -0.735851 | -0.679512 | 1.843496 | 0.175713 | -0.406328 | -0.746889 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.548062 | 1.314357 | 0.647061 | -1.384671 | -1.389505 | -0.642012 | 2.548062 | -0.424224 | -0.716697 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 1.904206 | 1.102310 | 0.474431 | -1.116515 | -1.434436 | -0.394150 | 1.904206 | -1.035818 | -0.985730 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.212114 | 0.478320 | 1.203975 | -1.332878 | -1.159263 | 2.212114 | -0.285851 | -0.812032 | -0.530970 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.330524 | 1.260284 | 0.637866 | -1.287564 | -1.409298 | -0.217539 | 2.330524 | -0.218388 | -0.706679 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 2.141497 | 0.634514 | 1.341861 | -1.425297 | -1.128046 | 2.141497 | -0.764833 | -0.721266 | -0.271560 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | N03 | digital_ok | ee Temporal Variability | 3.208537 | 1.028170 | 0.508577 | -1.436914 | -1.290859 | -0.641899 | 3.208537 | -0.701754 | -0.992429 |